{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ZWkBGKmqUZK"
   },
   "source": [
    "# Latent Class Analysis with StepMix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4GDka7-bKKCm"
   },
   "source": [
    "\n",
    "## Introduction\n",
    "\n",
    "\n",
    "In this notebook, we implement a latent class analysis (LCA). Informally, LCA models assume that an unobserved discrete random variable Z (the latent class, group or cluster) can explain some observed features X, as depicted in the following graphical model :\n",
    "\n",
    "<img src='https://drive.google.com/uc?id=1bRyxV9QNkHycf6UZ7fHnfEzEi89i83U0' width=150px>\n",
    "\n",
    "Members of the same latent group will exhibit similar patterns in the observed data X.\n",
    "\n",
    "Here, we perform a grid search over n_omponents to select the optimum number of groups. The serach revealed 3 clusters. We use a 15-85 split with a CV=3 for the test and training data and compare f1-score and weighted precision, accuracy. \n",
    "We impute the missing values using the simple imputation technique with the most frequent as our strategy and run the model seperately on the imputed data and the data without imputation to establish gound truth. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "id": "LZiL0p2tUUuJ"
   },
   "outputs": [],
   "source": [
    "# Python imports of libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import rand_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV, ParameterGrid\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.inspection import DecisionBoundaryDisplay\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve, auc, confusion_matrix\n",
    "from sklearn.metrics import adjusted_rand_score, completeness_score, homogeneity_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from stepmix.stepmix import StepMix\n",
    "\n",
    "from stepmix.utils import identify_coef\n",
    "from stepmix.utils import get_mixed_descriptor\n",
    "from stepmix.bootstrap import blrt_sweep\n",
    "from stepmix.stepmix import StepMixClassifier\n",
    "\n",
    "from scipy.stats import f_oneway\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ijugWOgJk_L-"
   },
   "source": [
    "<a name=\"data\"></a>\n",
    "## Setup the Survey Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "id": "r7c6pWpmU2qQ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>responseid</th>\n",
       "      <th>size</th>\n",
       "      <th>gender</th>\n",
       "      <th>intention_vet</th>\n",
       "      <th>sel_invest</th>\n",
       "      <th>livestock_insurance</th>\n",
       "      <th>report_disease</th>\n",
       "      <th>risk_perception</th>\n",
       "      <th>trust</th>\n",
       "      <th>knowledge_biosecurity</th>\n",
       "      <th>neigbhour_biosecure</th>\n",
       "      <th>eradication_program</th>\n",
       "      <th>income_derived_production</th>\n",
       "      <th>age</th>\n",
       "      <th>lenght_cu</th>\n",
       "      <th>weight</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R_2QYfqHr5UEjr64q</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Disagree</td>\n",
       "      <td>Strongly Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>None</td>\n",
       "      <td>Medium income share</td>\n",
       "      <td>51.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1355.000000</td>\n",
       "      <td>nebraska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R_1E07OA6iUSETxtS</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>Small income share</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.137500</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R_126lv83cBypI2DC</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>Small income share</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.432432</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R_u1BSatDo3eiEkOR</td>\n",
       "      <td>small</td>\n",
       "      <td>1. Female</td>\n",
       "      <td>Strongly Disagree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>High income share</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>westvirginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R_ZmcxF1WDVvh7fHz</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Strongly Disagree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>High income share</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.432432</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>R_1Q005ic8igrccHj</td>\n",
       "      <td>medium</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>Small income share</td>\n",
       "      <td>36.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>R_3dQD9gnsaElJPgf</td>\n",
       "      <td>small</td>\n",
       "      <td>1. Female</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>None</td>\n",
       "      <td>Small income share</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>R_2ZPmF37RSRdHK6m</td>\n",
       "      <td>small</td>\n",
       "      <td>1. Female</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>Small income share</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.432432</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>R_PCFK7JzxuKFkuMV</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Unsure</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>Medium income share</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.137500</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>R_3HRN995HryP92r3</td>\n",
       "      <td>small</td>\n",
       "      <td>2. Male</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>At least one eradication program</td>\n",
       "      <td>High income share</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            responseid    size     gender      intention_vet      sel_invest   \n",
       "0    R_2QYfqHr5UEjr64q   small    2. Male     Strongly Agree  Strongly Agree  \\\n",
       "1    R_1E07OA6iUSETxtS   small    2. Male              Agree        Disagree   \n",
       "2    R_126lv83cBypI2DC   small    2. Male             Unsure          Unsure   \n",
       "3    R_u1BSatDo3eiEkOR   small  1. Female  Strongly Disagree  Strongly Agree   \n",
       "4    R_ZmcxF1WDVvh7fHz   small    2. Male  Strongly Disagree  Strongly Agree   \n",
       "..                 ...     ...        ...                ...             ...   \n",
       "419  R_1Q005ic8igrccHj  medium    2. Male     Strongly Agree           Agree   \n",
       "420  R_3dQD9gnsaElJPgf   small  1. Female             Unsure          Unsure   \n",
       "421  R_2ZPmF37RSRdHK6m   small  1. Female             Unsure          Unsure   \n",
       "422  R_PCFK7JzxuKFkuMV   small    2. Male              Agree          Unsure   \n",
       "423  R_3HRN995HryP92r3   small    2. Male              Agree           Agree   \n",
       "\n",
       "    livestock_insurance     report_disease    risk_perception     trust   \n",
       "0     Strongly Disagree  Strongly Disagree           Disagree    Unsure  \\\n",
       "1                 Agree              Agree           Disagree  Disagree   \n",
       "2              Disagree              Agree             Unsure  Disagree   \n",
       "3        Strongly Agree     Strongly Agree  Strongly Disagree    Unsure   \n",
       "4        Strongly Agree     Strongly Agree              Agree     Agree   \n",
       "..                  ...                ...                ...       ...   \n",
       "419            Disagree           Disagree             Unsure    Unsure   \n",
       "420            Disagree             Unsure             Unsure  Disagree   \n",
       "421              Unsure             Unsure             Unsure    Unsure   \n",
       "422              Unsure              Agree             Unsure     Agree   \n",
       "423               Agree     Strongly Agree              Agree  Disagree   \n",
       "\n",
       "    knowledge_biosecurity neigbhour_biosecure   \n",
       "0          Strongly Agree      Strongly Agree  \\\n",
       "1                  Unsure            Disagree   \n",
       "2                  Unsure            Disagree   \n",
       "3                   Agree               Agree   \n",
       "4          Strongly Agree               Agree   \n",
       "..                    ...                 ...   \n",
       "419                 Agree            Disagree   \n",
       "420                Unsure              Unsure   \n",
       "421                Unsure            Disagree   \n",
       "422                 Agree               Agree   \n",
       "423                 Agree               Agree   \n",
       "\n",
       "                  eradication_program income_derived_production   age   \n",
       "0                                None       Medium income share  51.0  \\\n",
       "1    At least one eradication program        Small income share  38.0   \n",
       "2    At least one eradication program        Small income share  25.0   \n",
       "3    At least one eradication program         High income share  31.0   \n",
       "4    At least one eradication program         High income share  32.0   \n",
       "..                                ...                       ...   ...   \n",
       "419  At least one eradication program        Small income share  36.0   \n",
       "420                              None        Small income share  34.0   \n",
       "421  At least one eradication program        Small income share  28.0   \n",
       "422  At least one eradication program       Medium income share  30.0   \n",
       "423  At least one eradication program         High income share  28.0   \n",
       "\n",
       "     lenght_cu       weight         state  \n",
       "0         25.0  1355.000000      nebraska  \n",
       "1          4.0     1.137500    California  \n",
       "2          6.0     1.432432      Arkansas  \n",
       "3          5.0     1.333333  westvirginia  \n",
       "4          5.0     1.432432      Arkansas  \n",
       "..         ...          ...           ...  \n",
       "419       10.0     1.000000        nevada  \n",
       "420        7.0     0.083333        Alaska  \n",
       "421        6.0     1.432432      Arkansas  \n",
       "422       10.0     1.137500    California  \n",
       "423        6.0     0.083333        Alaska  \n",
       "\n",
       "[424 rows x 17 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Overview of the data\n",
    "data = pd.read_stata(\"dataset.dta\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 424 entries, 0 to 423\n",
      "Data columns (total 17 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   responseid                 424 non-null    object  \n",
      " 1   size                       420 non-null    category\n",
      " 2   gender                     422 non-null    category\n",
      " 3   intention_vet              422 non-null    category\n",
      " 4   sel_invest                 422 non-null    category\n",
      " 5   livestock_insurance        422 non-null    category\n",
      " 6   report_disease             422 non-null    category\n",
      " 7   risk_perception            422 non-null    category\n",
      " 8   trust                      422 non-null    category\n",
      " 9   knowledge_biosecurity      422 non-null    category\n",
      " 10  neigbhour_biosecure        422 non-null    category\n",
      " 11  eradication_program        422 non-null    category\n",
      " 12  income_derived_production  422 non-null    category\n",
      " 13  age                        422 non-null    float64 \n",
      " 14  lenght_cu                  414 non-null    float64 \n",
      " 15  weight                     411 non-null    float32 \n",
      " 16  state                      424 non-null    object  \n",
      "dtypes: category(12), float32(1), float64(2), object(2)\n",
      "memory usage: 22.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Overiew of the variables\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns in the DataFrame for better clarity or shorter names.\n",
    "data.rename(columns={'sel_invest': 'ind1', \n",
    "                   'livestock_insurance': 'ind2', \n",
    "                   'report_disease': 'ind3', \n",
    "                   'intention_vet': 'intention', \n",
    "                   'knowledge_biosecurity': 'fid', \n",
    "                   'trust': 'trust',\n",
    "                   'neigbhour_biosecure': 'confid',\n",
    "                   'risk_perception': 'riskp'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7500x4200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecting the variables to plot for descriptive statistics\n",
    "\n",
    "df = data[['intention', 'ind1', 'fid', 'ind2', 'ind3', 'trust', 'riskp', 'confid']]\n",
    "\n",
    "# Define the new x-axis labels\n",
    "x_labels = {\n",
    "    'intention': 'Intention to call a veterinarian',\n",
    "    'ind1': 'Motivation to self-invest in biosecurity',\n",
    "    'ind2': 'Likelihood of buying livestock insurance',\n",
    "    'ind3': 'Readiness to report disease',\n",
    "    'fid': 'Farmer knowledge',\n",
    "    'trust': 'Trust in government agencies',\n",
    "    'riskp' : 'Risk perception',\n",
    "    'confid': 'Confidence in neighbor to biosecure'\n",
    "}\n",
    "\n",
    "label_mapping = {\n",
    "    1: 'Strongly Disagree',\n",
    "    2: 'Disagree',\n",
    "    3: 'Unsure',\n",
    "    4: 'Agree',\n",
    "    5: 'Strongly Agree'\n",
    "}\n",
    "\n",
    "# Calculate the frequencies\n",
    "frequencies = df.apply(pd.Series.value_counts, normalize=True) * 100\n",
    "\n",
    "# Set the color palette\n",
    "colors = ['#01529D', '#d62728', '#2ca02c', '#FF7F0E', '#241A11', '#C87C54', '#9467bd', '#8c564b']\n",
    "\n",
    "\n",
    "# Create the figure\n",
    "fig, axes = plt.subplots(2, 4, figsize=(25, 14), dpi=300)\n",
    "\n",
    "# Plot each bar chart\n",
    "for i, variable in enumerate(df.columns):\n",
    "    row = i // 4\n",
    "    col = i % 4\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    frequencies[variable].plot(kind='bar', ax=ax, alpha=0.7, color=colors[i])\n",
    "    \n",
    "    ax.set_xlabel(x_labels.get(variable, variable), fontsize=12, fontweight='bold')\n",
    "    ax.set_ylabel('Percentage of responses', fontsize=12, fontweight='bold')\n",
    "    ax.set_xticklabels([label_mapping.get(tick, tick) for tick in frequencies.index], rotation=0, fontweight='bold')\n",
    "    \n",
    "    # Add percentage values on top of each bar\n",
    "    for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.annotate(f'{height:.1f}%', (p.get_x() + p.get_width() / 2, height),\n",
    "                    ha='center', va='bottom', fontsize=10, fontweight='bold')  \n",
    "   \n",
    "    ax.grid(False)\n",
    "\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('des.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    intention ind1 fid ind2 ind3 trust riskp confid\n",
      "419         5    4   4    2    2     3     3      2\n",
      "420         3    3   3    2    3     2     3      3\n",
      "421         3    3   3    3    3     3     3      2\n",
      "422         4    3   4    3    4     4     3      4\n",
      "423         4    4   4    4    5     2     4      4\n"
     ]
    }
   ],
   "source": [
    "# Mapping dictionary of categorical response\n",
    "mapping = {\n",
    "    'Strongly Agree': 5,\n",
    "    'Agree': 4,\n",
    "    'Unsure': 3,\n",
    "    'Disagree': 2,\n",
    "    'Strongly Disagree': 1\n",
    "}\n",
    "\n",
    "# Replace values in the DataFrame\n",
    "df.replace(mapping, inplace=True)\n",
    "\n",
    "print(df.tail(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tabulate the values\n",
    "#for var in df:\n",
    "    #print(df[var].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    intention ind1 fid ind2 ind3 trust riskp confid\n",
      "0           5    5   5    1    1     3     2      5\n",
      "1           4    2   3    4    4     2     2      2\n",
      "2           3    3   3    2    4     2     3      2\n",
      "3           1    5   4    5    5     3     1      4\n",
      "4           1    5   5    5    5     4     4      4\n",
      "..        ...  ...  ..  ...  ...   ...   ...    ...\n",
      "419         5    4   4    2    2     3     3      2\n",
      "420         3    3   3    2    3     2     3      3\n",
      "421         3    3   3    3    3     3     3      2\n",
      "422         4    3   4    3    4     4     3      4\n",
      "423         4    4   4    4    5     2     4      4\n",
      "\n",
      "[424 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "#Looping through to dataset to drop \"None\" Does not apply and -99\n",
    "values_to_drop = ['None', 'Does not apply', '-99']\n",
    "\n",
    "for col in df.columns:\n",
    "    for value in values_to_drop:\n",
    "        if value in df[col].values:\n",
    "            df = df[df[col] != value]\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BhhKqLfyrrGp"
   },
   "source": [
    "<a name=\"categorical\"></a>\n",
    "## LCA with Categorical Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-xnNAXb7VpMv"
   },
   "source": [
    "StepMix also supports categorical variables with more than 2 categories. Categories must be integer-encoded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#encoding = {\n",
    "    1.0: \"Strongly Disagree\",\n",
    "    2.0: \"Disagree\",\n",
    "    3.0: \"Unsure\",\n",
    "    4.0: \"Agree\",\n",
    "    5.0: \"Strongly Agree\"\n",
    "#}\n",
    "\n",
    "#df_encoded = df.replace({\n",
    "    'intention': encoding,\n",
    "    'ind1': encoding,\n",
    "    'ind2': encoding,\n",
    "    'ind3': encoding,\n",
    "    'fid': encoding,\n",
    "    'trust': encoding\n",
    "#})\n",
    "\n",
    "#print(df_encoded)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13.2\n",
      "3.8.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sns.__version__)\n",
    "print(mpl.__version__)\n",
    "\n",
    "#Correlation heatmap\n",
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', square=True, mask=False, linewidth=.5)\n",
    "plt.title('Correlation Heatmap of the Variables')\n",
    "plt.xticks(np.arange(len(df.columns)), ['Intention to call \\na Veterinarian to \\nexamine herd', 'Motivation to \\nself-invest \\nin biosecurity', 'Likelihood of \\nbuying livestock \\ninsurance', 'Readiness to report \\nsuspected infected \\nlivestock early', 'Farmer knowledge \\nabout biosecurity', 'Trust in \\ngovernment \\nagencies ', 'Risk \\nperception', 'Confidence in \\nneighbor to \\nbiosecure'], rotation=90, fontsize =11)\n",
    "plt.yticks(np.arange(len(df.columns)), ['Intention to call \\na Veterinarian to \\nexamine herd', 'Motivation to \\nself-invest \\nin biosecurity', 'Likelihood of \\nbuying livestock \\ninsurance', 'Readiness to report \\nsuspected infected \\nlivestock early', 'Farmer knowledge \\nabout biosecurity', 'Trust in \\ngovernment \\nagencies',  'Risk \\nperception', 'Confidence in \\nneighbor to \\nbiosecure'], rotation=360, fontsize =11)\n",
    "\n",
    "#plt.xaxis.set_major_locator(ticker.IndexLocator(base=0.5, offset=0.5))\n",
    "\n",
    "plt.savefig('cor.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00, 130.68it/s, max_LL=-4.04e+3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.69it/s, max_LL=-3.73e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  4.26it/s, max_LL=-3.56e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.40it/s, max_LL=-3.51e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  2.65it/s, max_LL=-3.44e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.43it/s, max_LL=-3.4e+3, \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  6.37it/s, max_LL=-3.38e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  4.27it/s, max_LL=-3.34e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.86it/s, max_LL=-3.34e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  2.19it/s, max_LL=-3.31e+3,"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of classes based on BIC: 3\n",
      "Optimal number of classes based on AIC: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Determining the optimal number of classes using the grid search\n",
    "n_components_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "# Drop rows with missing values (NaN)\n",
    "df = df.dropna()  \n",
    "\n",
    "bic_values = []\n",
    "aic_values = []\n",
    "\n",
    "#Note that the variables are categorical that is why the measurement is categorical and the structural also multinoulli\n",
    "for n_components in n_components_range:\n",
    "  model = StepMix(n_components=n_components, measurement=\"categorical\", verbose=0, random_state=42, structural='multinoulli', n_steps=2, assignment='soft', correction='BCH')\n",
    "  model.fit(df)\n",
    "  bic_values.append(model.bic(df))\n",
    "  aic_values.append(model.aic(df))\n",
    "\n",
    "# Find the optimal number of components based on BIC and AIC\n",
    "optimal_bic_components = np.argmin(bic_values) + 1\n",
    "optimal_aic_components = np.argmin(aic_values) + 1\n",
    "\n",
    "print(\"Optimal number of classes based on BIC:\", optimal_bic_components)\n",
    "print(\"Optimal number of classes based on AIC:\", optimal_aic_components)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the the optimal number classes\n",
    "# Compute change or delta for BIC and AIC\n",
    "delta_bic = np.array(bic_values) - min(bic_values)\n",
    "delta_aic = np.array(aic_values) - min(aic_values)\n",
    "\n",
    "# Plot BIC and AIC values\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# Plot BIC values\n",
    "ax1.plot(n_components_range, bic_values, label='BIC')\n",
    "ax1.set_xlabel('Number of Classes', fontsize=11, fontweight=\"bold\")\n",
    "ax1.set_ylabel('BIC Value', fontsize=11, fontweight=\"bold\")\n",
    "ax1.set_title('Optimal BIC Values', fontsize=11, fontweight=\"bold\")\n",
    "ax1.legend()\n",
    "ax1.grid(False)\n",
    "\n",
    "# Plot AIC values\n",
    "ax2.plot(n_components_range, aic_values, label='AIC')\n",
    "ax2.set_xlabel('Number of Classes', fontsize=11, fontweight=\"bold\")\n",
    "ax2.set_ylabel('AIC Value', fontsize=11, fontweight=\"bold\")\n",
    "ax2.set_title('Optimal AIC Values', fontsize=11, fontweight=\"bold\")\n",
    "ax2.legend()\n",
    "ax2.grid(False)\n",
    "\n",
    "# Plot delta BIC and AIC\n",
    "ax3.plot(n_components_range, delta_bic, marker='o', label='Delta BIC')\n",
    "ax3.plot(n_components_range, delta_aic, marker='x', label='Delta AIC')\n",
    "ax3.set_xlabel('Number of Classes', fontsize=11, fontweight=\"bold\")\n",
    "ax3.set_ylabel('Change in Score', fontsize=11, fontweight=\"bold\")\n",
    "ax3.set_title('Change in BIC and AIC Scores and Number of Classes', fontsize=11, fontweight=\"bold\")\n",
    "ax3.legend()\n",
    "ax3.grid(False)\n",
    "\n",
    "# Add dotted vertical lines for optimal components based on BIC and AIC\n",
    "optimal_bic_component_x = n_components_range[optimal_bic_components - 1]\n",
    "optimal_aic_component_x = n_components_range[optimal_aic_components - 1]\n",
    "\n",
    "ax3.axvline(optimal_bic_component_x, color='b', linestyle='--', label='Optimal BIC Components')\n",
    "ax3.axvline(optimal_aic_component_x, color='k', linestyle='--', label='Optimal AIC Components')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('mplt1.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                \n",
      "          class_no                    0       1       2\n",
      "          param variable                               \n",
      "          pis   confid_1         0.1073  0.0000  0.0583\n",
      "                confid_2         0.3271  0.0090  0.0694\n",
      "                confid_3         0.4013  0.2692  0.1359\n",
      "                confid_4         0.1407  0.6755  0.3545\n",
      "                confid_5         0.0236  0.0463  0.3818\n",
      "                fid_1            0.0111  0.0000  0.0000\n",
      "                fid_2            0.1443  0.0268  0.0555\n",
      "                fid_3            0.5102  0.1379  0.0304\n",
      "                fid_4            0.2472  0.7498  0.3251\n",
      "                fid_5            0.0872  0.0855  0.5891\n",
      "                ind1_1           0.0111  0.0000  0.0092\n",
      "                ind1_2           0.1899  0.0086  0.0000\n",
      "                ind1_3           0.5079  0.0730  0.0000\n",
      "                ind1_4           0.2612  0.8197  0.2804\n",
      "                ind1_5           0.0299  0.0987  0.7104\n",
      "                ind2_1           0.0000  0.0000  0.0184\n",
      "                ind2_2           0.2298  0.0083  0.0411\n",
      "                ind2_3           0.4340  0.2137  0.0389\n",
      "                ind2_4           0.2740  0.5658  0.3404\n",
      "                ind2_5           0.0622  0.2122  0.5612\n",
      "                ind3_1           0.0000  0.0000  0.0276\n",
      "                ind3_2           0.0667  0.0000  0.0000\n",
      "                ind3_3           0.3151  0.1104  0.0000\n",
      "                ind3_4           0.5448  0.6063  0.2079\n",
      "                ind3_5           0.0734  0.2833  0.7646\n",
      "                intention_1      0.0000  0.0000  0.0460\n",
      "                intention_2      0.0778  0.0000  0.0184\n",
      "                intention_3      0.2310  0.0698  0.0241\n",
      "                intention_4      0.5488  0.7831  0.3015\n",
      "                intention_5      0.1425  0.1471  0.6100\n",
      "                riskp_1          0.0578  0.0000  0.1176\n",
      "                riskp_2          0.2506  0.0357  0.1147\n",
      "                riskp_3          0.5831  0.1895  0.1491\n",
      "                riskp_4          0.1085  0.7315  0.1650\n",
      "                riskp_5          0.0000  0.0433  0.4536\n",
      "                trust_1          0.0854  0.0148  0.0000\n",
      "                trust_2          0.2258  0.0387  0.0096\n",
      "                trust_3          0.5678  0.3199  0.0780\n",
      "                trust_4          0.0935  0.5718  0.3856\n",
      "                trust_5          0.0276  0.0547  0.5268\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.21\n",
      "        Class 2 : 0.53\n",
      "        Class 3 : 0.26\n",
      "    ============================================================================\n",
      "    Fit for 3 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 3\n",
      "    Number of estimated parameters: 122\n",
      "    Log-likelihood (LL)           : -3555.8945\n",
      "    -2LL                          : 7111.7889\n",
      "    Average LL                    : -8.4263\n",
      "    AIC                           : 7355.79\n",
      "    BIC                           : 7849.28\n",
      "    CAIC                          : 7971.28\n",
      "    Sample-Size Adjusted BIC      : 8199.62\n",
      "    Entropy                       : 67.6939\n",
      "    Scaled Relative Entropy       : 0.8540\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "                  progress_bar=0, random_state=42, structural=&#x27;categorical&#x27;,\n",
       "                  verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StepMixClassifier</label><div class=\"sk-toggleable__content\"><pre>StepMixClassifier(measurement=&#x27;multinoulli&#x27;, n_components=3, n_steps=2,\n",
       "                  progress_bar=0, random_state=42, structural=&#x27;categorical&#x27;,\n",
       "                  verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StepMixClassifier(measurement='multinoulli', n_components=3, n_steps=2,\n",
       "                  progress_bar=0, random_state=42, structural='categorical',\n",
       "                  verbose=1)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit StepMix model\n",
    "clf = StepMixClassifier(n_components=3, measurement='multinoulli', structural='categorical', n_steps = 2, random_state=42, verbose=1, progress_bar=0)\n",
    "clf.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define base model for bootstrapped likelihood ratio test\n",
    "model = StepMix(n_components=3, n_steps=1, measurement='categorical', assignment='soft',\n",
    "                structural='multinoulli', random_state=42, verbose=0, progress_bar=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 1 vs. 2 classes...\n",
      "Bootstrapping null model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:00<00:00, 32.27it/s, max_LL=-3.95e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bootstrapping alternative model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:09<00:00,  2.07it/s, max_LL=-3.92e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 2 vs. 3 classes...\n",
      "Bootstrapping null model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:01<00:00, 10.93it/s, max_LL=-3.64e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bootstrapping alternative model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:07<00:00,  2.53it/s, max_LL=-3.62e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 3 vs. 4 classes...\n",
      "Bootstrapping null model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:03<00:00,  6.12it/s, max_LL=-3.43e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bootstrapping alternative model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:08<00:00,  2.28it/s, max_LL=-3.42e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 4 vs. 5 classes...\n",
      "Bootstrapping null model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:05<00:00,  3.67it/s, max_LL=-3.42e+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bootstrapping alternative model...\n",
      "\n",
      "Bootstrapping estimator...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bootstrap Repetitions    : 100%|█| 20/20 [00:08<00:00,  2.34it/s, max_LL=-3.36e+"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "BLRT Sweep Results\n",
      "                    p\n",
      "Test                 \n",
      "1 vs. 2 classes  0.00\n",
      "2 vs. 3 classes  0.00\n",
      "3 vs. 4 classes  0.00\n",
      "4 vs. 5 classes  0.05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Run BLRT sweep from 1 class to 5 classes\n",
    "# Expect some convergence warnings\n",
    "p_values = blrt_sweep(model, df, low=1, high=5, n_repetitions=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 vs. 2 classes</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 vs. 3 classes</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 vs. 4 classes</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 vs. 5 classes</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     p\n",
       "Test                  \n",
       "1 vs. 2 classes   True\n",
       "2 vs. 3 classes   True\n",
       "3 vs. 4 classes   True\n",
       "4 vs. 5 classes  False"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P-values less that 5%\n",
    "p_values < .05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.59it/s, max_LL=-3.73e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical        \n",
      "          class_no                    0       1\n",
      "          param variable                       \n",
      "          pis   confid_1         0.0413  0.0330\n",
      "                confid_2         0.1232  0.0467\n",
      "                confid_3         0.3691  0.1057\n",
      "                confid_4         0.4452  0.5284\n",
      "                confid_5         0.0213  0.2863\n",
      "                fid_1            0.0040  0.0000\n",
      "                fid_2            0.0745  0.0365\n",
      "                fid_3            0.3009  0.0244\n",
      "                fid_4            0.5589  0.4950\n",
      "                fid_5            0.0617  0.4441\n",
      "                ind1_1           0.0040  0.0059\n",
      "                ind1_2           0.0754  0.0000\n",
      "                ind1_3           0.2361  0.0145\n",
      "                ind1_4           0.6367  0.4502\n",
      "                ind1_5           0.0479  0.5294\n",
      "                ind2_1           0.0000  0.0118\n",
      "                ind2_2           0.0925  0.0217\n",
      "                ind2_3           0.3236  0.0554\n",
      "                ind2_4           0.4555  0.4306\n",
      "                ind2_5           0.1284  0.4805\n",
      "                ind3_1           0.0000  0.0177\n",
      "                ind3_2           0.0238  0.0000\n",
      "                ind3_3           0.2036  0.0098\n",
      "                ind3_4           0.6317  0.2809\n",
      "                ind3_5           0.1408  0.6916\n",
      "                intention_1      0.0000  0.0294\n",
      "                intention_2      0.0278  0.0118\n",
      "                intention_3      0.1378  0.0250\n",
      "                intention_4      0.7019  0.4712\n",
      "                intention_5      0.1326  0.4626\n",
      "                riskp_1          0.0227  0.0723\n",
      "                riskp_2          0.1173  0.0791\n",
      "                riskp_3          0.3688  0.1060\n",
      "                riskp_4          0.4912  0.3953\n",
      "                riskp_5          0.0000  0.3473\n",
      "                trust_1          0.0436  0.0000\n",
      "                trust_2          0.1136  0.0080\n",
      "                trust_3          0.4673  0.0776\n",
      "                trust_4          0.3549  0.5212\n",
      "                trust_5          0.0206  0.3933\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.60\n",
      "        Class 2 : 0.40\n",
      "    ============================================================================\n",
      "    Fit for 2 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 2\n",
      "    Number of estimated parameters: 81\n",
      "    Log-likelihood (LL)           : -3730.4855\n",
      "    -2LL                          : 7460.9709\n",
      "    Average LL                    : -8.8400\n",
      "    AIC                           : 7622.97\n",
      "    BIC                           : 7950.62\n",
      "    CAIC                          : 8031.62\n",
      "    Sample-Size Adjusted BIC      : 8183.22\n",
      "    Entropy                       : 59.4080\n",
      "    Scaled Relative Entropy       : 0.7969\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  4.42it/s, max_LL=-3.56e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                \n",
      "          class_no                    0       1       2\n",
      "          param variable                               \n",
      "          pis   confid_1         0.1073  0.0000  0.0583\n",
      "                confid_2         0.3271  0.0090  0.0694\n",
      "                confid_3         0.4013  0.2692  0.1359\n",
      "                confid_4         0.1407  0.6755  0.3545\n",
      "                confid_5         0.0236  0.0463  0.3818\n",
      "                fid_1            0.0111  0.0000  0.0000\n",
      "                fid_2            0.1443  0.0268  0.0555\n",
      "                fid_3            0.5102  0.1379  0.0304\n",
      "                fid_4            0.2472  0.7498  0.3251\n",
      "                fid_5            0.0872  0.0855  0.5891\n",
      "                ind1_1           0.0111  0.0000  0.0092\n",
      "                ind1_2           0.1899  0.0086  0.0000\n",
      "                ind1_3           0.5079  0.0730  0.0000\n",
      "                ind1_4           0.2612  0.8197  0.2804\n",
      "                ind1_5           0.0299  0.0987  0.7104\n",
      "                ind2_1           0.0000  0.0000  0.0184\n",
      "                ind2_2           0.2298  0.0083  0.0411\n",
      "                ind2_3           0.4340  0.2137  0.0389\n",
      "                ind2_4           0.2740  0.5658  0.3404\n",
      "                ind2_5           0.0622  0.2122  0.5612\n",
      "                ind3_1           0.0000  0.0000  0.0276\n",
      "                ind3_2           0.0667  0.0000  0.0000\n",
      "                ind3_3           0.3151  0.1104  0.0000\n",
      "                ind3_4           0.5448  0.6063  0.2079\n",
      "                ind3_5           0.0734  0.2833  0.7646\n",
      "                intention_1      0.0000  0.0000  0.0460\n",
      "                intention_2      0.0778  0.0000  0.0184\n",
      "                intention_3      0.2310  0.0698  0.0241\n",
      "                intention_4      0.5488  0.7831  0.3015\n",
      "                intention_5      0.1425  0.1471  0.6100\n",
      "                riskp_1          0.0578  0.0000  0.1176\n",
      "                riskp_2          0.2506  0.0357  0.1147\n",
      "                riskp_3          0.5831  0.1895  0.1491\n",
      "                riskp_4          0.1085  0.7315  0.1650\n",
      "                riskp_5          0.0000  0.0433  0.4536\n",
      "                trust_1          0.0854  0.0148  0.0000\n",
      "                trust_2          0.2258  0.0387  0.0096\n",
      "                trust_3          0.5678  0.3199  0.0780\n",
      "                trust_4          0.0935  0.5718  0.3856\n",
      "                trust_5          0.0276  0.0547  0.5268\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.21\n",
      "        Class 2 : 0.53\n",
      "        Class 3 : 0.26\n",
      "    ============================================================================\n",
      "    Fit for 3 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 3\n",
      "    Number of estimated parameters: 122\n",
      "    Log-likelihood (LL)           : -3555.8945\n",
      "    -2LL                          : 7111.7889\n",
      "    Average LL                    : -8.4263\n",
      "    AIC                           : 7355.79\n",
      "    BIC                           : 7849.28\n",
      "    CAIC                          : 7971.28\n",
      "    Sample-Size Adjusted BIC      : 8199.62\n",
      "    Entropy                       : 67.6939\n",
      "    Scaled Relative Entropy       : 0.8540\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.71it/s, max_LL=-3.51e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                        \n",
      "          class_no                    0       1       2       3\n",
      "          param variable                                       \n",
      "          pis   confid_1         0.2360  0.0277  0.0926  0.0000\n",
      "                confid_2         0.3632  0.0000  0.3110  0.0164\n",
      "                confid_3         0.1931  0.1368  0.4394  0.2513\n",
      "                confid_4         0.1615  0.3578  0.1351  0.6769\n",
      "                confid_5         0.0462  0.4777  0.0220  0.0554\n",
      "                fid_1            0.0385  0.0000  0.0000  0.0000\n",
      "                fid_2            0.1777  0.0391  0.1378  0.0251\n",
      "                fid_3            0.0684  0.0233  0.5494  0.1321\n",
      "                fid_4            0.1525  0.3315  0.2716  0.7369\n",
      "                fid_5            0.5629  0.6061  0.0411  0.1058\n",
      "                ind1_1           0.0391  0.0124  0.0000  0.0000\n",
      "                ind1_2           0.1315  0.0000  0.1655  0.0081\n",
      "                ind1_3           0.0847  0.0000  0.5370  0.0657\n",
      "                ind1_4           0.5161  0.1369  0.2641  0.8168\n",
      "                ind1_5           0.2285  0.8507  0.0335  0.1094\n",
      "                ind2_1           0.0000  0.0251  0.0000  0.0000\n",
      "                ind2_2           0.2585  0.0183  0.2033  0.0085\n",
      "                ind2_3           0.1333  0.0256  0.4570  0.2040\n",
      "                ind2_4           0.6083  0.2262  0.2676  0.5652\n",
      "                ind2_5           0.0000  0.7047  0.0720  0.2223\n",
      "                ind3_1           0.0385  0.0251  0.0000  0.0000\n",
      "                ind3_2           0.0874  0.0000  0.0451  0.0000\n",
      "                ind3_3           0.1371  0.0000  0.3021  0.1045\n",
      "                ind3_4           0.3397  0.1372  0.5925  0.5915\n",
      "                ind3_5           0.3973  0.8377  0.0603  0.3039\n",
      "                intention_1      0.0340  0.0517  0.0000  0.0000\n",
      "                intention_2      0.0000  0.0251  0.0845  0.0000\n",
      "                intention_3      0.0000  0.0208  0.2622  0.0669\n",
      "                intention_4      0.4220  0.2416  0.5438  0.7781\n",
      "                intention_5      0.5440  0.6609  0.1095  0.1550\n",
      "                riskp_1          0.5075  0.0606  0.0000  0.0000\n",
      "                riskp_2          0.2363  0.0976  0.2503  0.0358\n",
      "                riskp_3          0.1694  0.1705  0.6205  0.1783\n",
      "                riskp_4          0.0867  0.1517  0.1292  0.7104\n",
      "                riskp_5          0.0000  0.5196  0.0000  0.0755\n",
      "                trust_1          0.0920  0.0000  0.0659  0.0135\n",
      "                trust_2          0.0000  0.0127  0.2505  0.0353\n",
      "                trust_3          0.1536  0.0664  0.6089  0.3052\n",
      "                trust_4          0.5617  0.2734  0.0747  0.5799\n",
      "                trust_5          0.1927  0.6476  0.0000  0.0661\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.06\n",
      "        Class 2 : 0.19\n",
      "        Class 3 : 0.20\n",
      "        Class 4 : 0.55\n",
      "    ============================================================================\n",
      "    Fit for 4 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 4\n",
      "    Number of estimated parameters: 163\n",
      "    Log-likelihood (LL)           : -3512.7113\n",
      "    -2LL                          : 7025.4225\n",
      "    Average LL                    : -8.3240\n",
      "    AIC                           : 7351.42\n",
      "    BIC                           : 8010.76\n",
      "    CAIC                          : 8173.76\n",
      "    Sample-Size Adjusted BIC      : 8478.84\n",
      "    Entropy                       : 70.0659\n",
      "    Scaled Relative Entropy       : 0.8802\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  2.87it/s, max_LL=-3.44e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                                \n",
      "          class_no                    0       1       2       3       4\n",
      "          param variable                                               \n",
      "          pis   confid_1         0.0000  0.0330  0.0000  0.1185  0.2168\n",
      "                confid_2         0.0183  0.0193  0.0072  0.4374  0.2780\n",
      "                confid_3         0.0000  0.1737  0.4147  0.3534  0.2664\n",
      "                confid_4         0.7870  0.2800  0.5698  0.0578  0.1933\n",
      "                confid_5         0.1947  0.4939  0.0082  0.0329  0.0455\n",
      "                fid_1            0.0000  0.0000  0.0000  0.0000  0.0310\n",
      "                fid_2            0.0000  0.0492  0.0421  0.1628  0.1609\n",
      "                fid_3            0.0027  0.0516  0.2317  0.5741  0.0942\n",
      "                fid_4            0.7404  0.2759  0.6753  0.1780  0.2770\n",
      "                fid_5            0.2569  0.6233  0.0508  0.0850  0.4370\n",
      "                ind1_1           0.0000  0.0161  0.0000  0.0000  0.0317\n",
      "                ind1_2           0.0196  0.0000  0.0060  0.2718  0.0000\n",
      "                ind1_3           0.0323  0.0000  0.1366  0.5618  0.0703\n",
      "                ind1_4           0.7149  0.0652  0.7996  0.1664  0.4843\n",
      "                ind1_5           0.2332  0.9187  0.0579  0.0000  0.4137\n",
      "                ind2_1           0.0000  0.0330  0.0000  0.0000  0.0000\n",
      "                ind2_2           0.0000  0.0000  0.0123  0.3034  0.2162\n",
      "                ind2_3           0.0623  0.0138  0.2932  0.4240  0.2714\n",
      "                ind2_4           0.6449  0.1283  0.5135  0.2034  0.5123\n",
      "                ind2_5           0.2928  0.8249  0.1810  0.0692  0.0000\n",
      "                ind3_1           0.0000  0.0165  0.0000  0.0000  0.0619\n",
      "                ind3_2           0.0000  0.0000  0.0170  0.0000  0.0956\n",
      "                ind3_3           0.0343  0.0000  0.1365  0.3786  0.1208\n",
      "                ind3_4           0.4268  0.0714  0.6686  0.5814  0.3617\n",
      "                ind3_5           0.5390  0.9121  0.1779  0.0400  0.3599\n",
      "                intention_1      0.0000  0.0661  0.0000  0.0000  0.0307\n",
      "                intention_2      0.0000  0.0330  0.0051  0.1039  0.0000\n",
      "                intention_3      0.0313  0.0000  0.1009  0.2565  0.1079\n",
      "                intention_4      0.8282  0.0952  0.7467  0.5633  0.2560\n",
      "                intention_5      0.1405  0.8057  0.1474  0.0763  0.6055\n",
      "                riskp_1          0.0192  0.0716  0.0000  0.0173  0.3325\n",
      "                riskp_2          0.0402  0.1042  0.0377  0.3499  0.1740\n",
      "                riskp_3          0.0000  0.2192  0.3164  0.5475  0.3478\n",
      "                riskp_4          0.6564  0.1152  0.6460  0.0853  0.1078\n",
      "                riskp_5          0.2842  0.4899  0.0000  0.0000  0.0379\n",
      "                trust_1          0.0112  0.0000  0.0215  0.1053  0.0000\n",
      "                trust_2          0.0003  0.0165  0.0670  0.2966  0.0000\n",
      "                trust_3          0.0000  0.1096  0.4770  0.5981  0.2288\n",
      "                trust_4          0.7562  0.1915  0.4295  0.0000  0.5602\n",
      "                trust_5          0.2323  0.6824  0.0051  0.0000  0.2110\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.23\n",
      "        Class 2 : 0.14\n",
      "        Class 3 : 0.41\n",
      "        Class 4 : 0.14\n",
      "        Class 5 : 0.08\n",
      "    ============================================================================\n",
      "    Fit for 5 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 5\n",
      "    Number of estimated parameters: 204\n",
      "    Log-likelihood (LL)           : -3435.7547\n",
      "    -2LL                          : 6871.5095\n",
      "    Average LL                    : -8.1416\n",
      "    AIC                           : 7279.51\n",
      "    BIC                           : 8104.69\n",
      "    CAIC                          : 8308.69\n",
      "    Sample-Size Adjusted BIC      : 8690.51\n",
      "    Entropy                       : 85.4566\n",
      "    Scaled Relative Entropy       : 0.8742\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looping and Estimating the latent classes from 2 to 5 and plotting the graph\n",
    "\n",
    "\n",
    "# Initialize an empty list to store the results\n",
    "results = []\n",
    "\n",
    "# Loop over different n_components values\n",
    "for n_components in [2, 3, 4, 5]:\n",
    "    model = StepMix(n_components=n_components, measurement=\"categorical\", verbose=1, random_state=42, structural='multinoulli', n_steps=2, assignment='soft', correction='BCH')\n",
    "    model.fit(df)\n",
    "    results.append(model.get_mm_df())\n",
    "\n",
    "# Create a subplot for each result\n",
    "fig, axs = plt.subplots(1, len(results), figsize=(20, 8))\n",
    "\n",
    "# Loop over the results and create a matshow for each\n",
    "for i, result in enumerate(results):\n",
    "    axs[i].matshow(result, cmap='coolwarm')\n",
    "    axs[i].set_title(f'n_components = {i+2}')\n",
    "\n",
    "# Adjust the layout to shrink the space between plots\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "# Save the plot\n",
    "plt.savefig('biosecurity_plot.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  9.06it/s, max_LL=-3.73e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical        \n",
      "          class_no                    0       1\n",
      "          param variable                       \n",
      "          pis   confid_1         0.0413  0.0330\n",
      "                confid_2         0.1232  0.0467\n",
      "                confid_3         0.3691  0.1057\n",
      "                confid_4         0.4452  0.5284\n",
      "                confid_5         0.0213  0.2863\n",
      "                fid_1            0.0040  0.0000\n",
      "                fid_2            0.0745  0.0365\n",
      "                fid_3            0.3009  0.0244\n",
      "                fid_4            0.5589  0.4950\n",
      "                fid_5            0.0617  0.4441\n",
      "                ind1_1           0.0040  0.0059\n",
      "                ind1_2           0.0754  0.0000\n",
      "                ind1_3           0.2361  0.0145\n",
      "                ind1_4           0.6367  0.4502\n",
      "                ind1_5           0.0479  0.5294\n",
      "                ind2_1           0.0000  0.0118\n",
      "                ind2_2           0.0925  0.0217\n",
      "                ind2_3           0.3236  0.0554\n",
      "                ind2_4           0.4555  0.4306\n",
      "                ind2_5           0.1284  0.4805\n",
      "                ind3_1           0.0000  0.0177\n",
      "                ind3_2           0.0238  0.0000\n",
      "                ind3_3           0.2036  0.0098\n",
      "                ind3_4           0.6317  0.2809\n",
      "                ind3_5           0.1408  0.6916\n",
      "                intention_1      0.0000  0.0294\n",
      "                intention_2      0.0278  0.0118\n",
      "                intention_3      0.1378  0.0250\n",
      "                intention_4      0.7019  0.4712\n",
      "                intention_5      0.1326  0.4626\n",
      "                riskp_1          0.0227  0.0723\n",
      "                riskp_2          0.1173  0.0791\n",
      "                riskp_3          0.3688  0.1060\n",
      "                riskp_4          0.4912  0.3953\n",
      "                riskp_5          0.0000  0.3473\n",
      "                trust_1          0.0436  0.0000\n",
      "                trust_2          0.1136  0.0080\n",
      "                trust_3          0.4673  0.0776\n",
      "                trust_4          0.3549  0.5212\n",
      "                trust_5          0.0206  0.3933\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.60\n",
      "        Class 2 : 0.40\n",
      "    ============================================================================\n",
      "    Fit for 2 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 2\n",
      "    Number of estimated parameters: 81\n",
      "    Log-likelihood (LL)           : -3730.4855\n",
      "    -2LL                          : 7460.9709\n",
      "    Average LL                    : -8.8400\n",
      "    AIC                           : 7622.97\n",
      "    BIC                           : 7950.62\n",
      "    CAIC                          : 8031.62\n",
      "    Sample-Size Adjusted BIC      : 8183.22\n",
      "    Entropy                       : 59.4080\n",
      "    Scaled Relative Entropy       : 0.7969\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00, 13.02it/s, max_LL=-3.56e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                \n",
      "          class_no                    0       1       2\n",
      "          param variable                               \n",
      "          pis   confid_1         0.1073  0.0000  0.0583\n",
      "                confid_2         0.3271  0.0090  0.0694\n",
      "                confid_3         0.4013  0.2692  0.1359\n",
      "                confid_4         0.1407  0.6755  0.3545\n",
      "                confid_5         0.0236  0.0463  0.3818\n",
      "                fid_1            0.0111  0.0000  0.0000\n",
      "                fid_2            0.1443  0.0268  0.0555\n",
      "                fid_3            0.5102  0.1379  0.0304\n",
      "                fid_4            0.2472  0.7498  0.3251\n",
      "                fid_5            0.0872  0.0855  0.5891\n",
      "                ind1_1           0.0111  0.0000  0.0092\n",
      "                ind1_2           0.1899  0.0086  0.0000\n",
      "                ind1_3           0.5079  0.0730  0.0000\n",
      "                ind1_4           0.2612  0.8197  0.2804\n",
      "                ind1_5           0.0299  0.0987  0.7104\n",
      "                ind2_1           0.0000  0.0000  0.0184\n",
      "                ind2_2           0.2298  0.0083  0.0411\n",
      "                ind2_3           0.4340  0.2137  0.0389\n",
      "                ind2_4           0.2740  0.5658  0.3404\n",
      "                ind2_5           0.0622  0.2122  0.5612\n",
      "                ind3_1           0.0000  0.0000  0.0276\n",
      "                ind3_2           0.0667  0.0000  0.0000\n",
      "                ind3_3           0.3151  0.1104  0.0000\n",
      "                ind3_4           0.5448  0.6063  0.2079\n",
      "                ind3_5           0.0734  0.2833  0.7646\n",
      "                intention_1      0.0000  0.0000  0.0460\n",
      "                intention_2      0.0778  0.0000  0.0184\n",
      "                intention_3      0.2310  0.0698  0.0241\n",
      "                intention_4      0.5488  0.7831  0.3015\n",
      "                intention_5      0.1425  0.1471  0.6100\n",
      "                riskp_1          0.0578  0.0000  0.1176\n",
      "                riskp_2          0.2506  0.0357  0.1147\n",
      "                riskp_3          0.5831  0.1895  0.1491\n",
      "                riskp_4          0.1085  0.7315  0.1650\n",
      "                riskp_5          0.0000  0.0433  0.4536\n",
      "                trust_1          0.0854  0.0148  0.0000\n",
      "                trust_2          0.2258  0.0387  0.0096\n",
      "                trust_3          0.5678  0.3199  0.0780\n",
      "                trust_4          0.0935  0.5718  0.3856\n",
      "                trust_5          0.0276  0.0547  0.5268\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.21\n",
      "        Class 2 : 0.53\n",
      "        Class 3 : 0.26\n",
      "    ============================================================================\n",
      "    Fit for 3 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 3\n",
      "    Number of estimated parameters: 122\n",
      "    Log-likelihood (LL)           : -3555.8945\n",
      "    -2LL                          : 7111.7889\n",
      "    Average LL                    : -8.4263\n",
      "    AIC                           : 7355.79\n",
      "    BIC                           : 7849.28\n",
      "    CAIC                          : 7971.28\n",
      "    Sample-Size Adjusted BIC      : 8199.62\n",
      "    Entropy                       : 67.6939\n",
      "    Scaled Relative Entropy       : 0.8540\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  5.36it/s, max_LL=-3.51e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                        \n",
      "          class_no                    0       1       2       3\n",
      "          param variable                                       \n",
      "          pis   confid_1         0.2360  0.0277  0.0926  0.0000\n",
      "                confid_2         0.3632  0.0000  0.3110  0.0164\n",
      "                confid_3         0.1931  0.1368  0.4394  0.2513\n",
      "                confid_4         0.1615  0.3578  0.1351  0.6769\n",
      "                confid_5         0.0462  0.4777  0.0220  0.0554\n",
      "                fid_1            0.0385  0.0000  0.0000  0.0000\n",
      "                fid_2            0.1777  0.0391  0.1378  0.0251\n",
      "                fid_3            0.0684  0.0233  0.5494  0.1321\n",
      "                fid_4            0.1525  0.3315  0.2716  0.7369\n",
      "                fid_5            0.5629  0.6061  0.0411  0.1058\n",
      "                ind1_1           0.0391  0.0124  0.0000  0.0000\n",
      "                ind1_2           0.1315  0.0000  0.1655  0.0081\n",
      "                ind1_3           0.0847  0.0000  0.5370  0.0657\n",
      "                ind1_4           0.5161  0.1369  0.2641  0.8168\n",
      "                ind1_5           0.2285  0.8507  0.0335  0.1094\n",
      "                ind2_1           0.0000  0.0251  0.0000  0.0000\n",
      "                ind2_2           0.2585  0.0183  0.2033  0.0085\n",
      "                ind2_3           0.1333  0.0256  0.4570  0.2040\n",
      "                ind2_4           0.6083  0.2262  0.2676  0.5652\n",
      "                ind2_5           0.0000  0.7047  0.0720  0.2223\n",
      "                ind3_1           0.0385  0.0251  0.0000  0.0000\n",
      "                ind3_2           0.0874  0.0000  0.0451  0.0000\n",
      "                ind3_3           0.1371  0.0000  0.3021  0.1045\n",
      "                ind3_4           0.3397  0.1372  0.5925  0.5915\n",
      "                ind3_5           0.3973  0.8377  0.0603  0.3039\n",
      "                intention_1      0.0340  0.0517  0.0000  0.0000\n",
      "                intention_2      0.0000  0.0251  0.0845  0.0000\n",
      "                intention_3      0.0000  0.0208  0.2622  0.0669\n",
      "                intention_4      0.4220  0.2416  0.5438  0.7781\n",
      "                intention_5      0.5440  0.6609  0.1095  0.1550\n",
      "                riskp_1          0.5075  0.0606  0.0000  0.0000\n",
      "                riskp_2          0.2363  0.0976  0.2503  0.0358\n",
      "                riskp_3          0.1694  0.1705  0.6205  0.1783\n",
      "                riskp_4          0.0867  0.1517  0.1292  0.7104\n",
      "                riskp_5          0.0000  0.5196  0.0000  0.0755\n",
      "                trust_1          0.0920  0.0000  0.0659  0.0135\n",
      "                trust_2          0.0000  0.0127  0.2505  0.0353\n",
      "                trust_3          0.1536  0.0664  0.6089  0.3052\n",
      "                trust_4          0.5617  0.2734  0.0747  0.5799\n",
      "                trust_5          0.1927  0.6476  0.0000  0.0661\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.06\n",
      "        Class 2 : 0.19\n",
      "        Class 3 : 0.20\n",
      "        Class 4 : 0.55\n",
      "    ============================================================================\n",
      "    Fit for 4 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 4\n",
      "    Number of estimated parameters: 163\n",
      "    Log-likelihood (LL)           : -3512.7113\n",
      "    -2LL                          : 7025.4225\n",
      "    Average LL                    : -8.3240\n",
      "    AIC                           : 7351.42\n",
      "    BIC                           : 8010.76\n",
      "    CAIC                          : 8173.76\n",
      "    Sample-Size Adjusted BIC      : 8478.84\n",
      "    Entropy                       : 70.0659\n",
      "    Scaled Relative Entropy       : 0.8802\n",
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  3.06it/s, max_LL=-3.44e+3,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                                \n",
      "          class_no                    0       1       2       3       4\n",
      "          param variable                                               \n",
      "          pis   confid_1         0.0000  0.0330  0.0000  0.1185  0.2168\n",
      "                confid_2         0.0183  0.0193  0.0072  0.4374  0.2780\n",
      "                confid_3         0.0000  0.1737  0.4147  0.3534  0.2664\n",
      "                confid_4         0.7870  0.2800  0.5698  0.0578  0.1933\n",
      "                confid_5         0.1947  0.4939  0.0082  0.0329  0.0455\n",
      "                fid_1            0.0000  0.0000  0.0000  0.0000  0.0310\n",
      "                fid_2            0.0000  0.0492  0.0421  0.1628  0.1609\n",
      "                fid_3            0.0027  0.0516  0.2317  0.5741  0.0942\n",
      "                fid_4            0.7404  0.2759  0.6753  0.1780  0.2770\n",
      "                fid_5            0.2569  0.6233  0.0508  0.0850  0.4370\n",
      "                ind1_1           0.0000  0.0161  0.0000  0.0000  0.0317\n",
      "                ind1_2           0.0196  0.0000  0.0060  0.2718  0.0000\n",
      "                ind1_3           0.0323  0.0000  0.1366  0.5618  0.0703\n",
      "                ind1_4           0.7149  0.0652  0.7996  0.1664  0.4843\n",
      "                ind1_5           0.2332  0.9187  0.0579  0.0000  0.4137\n",
      "                ind2_1           0.0000  0.0330  0.0000  0.0000  0.0000\n",
      "                ind2_2           0.0000  0.0000  0.0123  0.3034  0.2162\n",
      "                ind2_3           0.0623  0.0138  0.2932  0.4240  0.2714\n",
      "                ind2_4           0.6449  0.1283  0.5135  0.2034  0.5123\n",
      "                ind2_5           0.2928  0.8249  0.1810  0.0692  0.0000\n",
      "                ind3_1           0.0000  0.0165  0.0000  0.0000  0.0619\n",
      "                ind3_2           0.0000  0.0000  0.0170  0.0000  0.0956\n",
      "                ind3_3           0.0343  0.0000  0.1365  0.3786  0.1208\n",
      "                ind3_4           0.4268  0.0714  0.6686  0.5814  0.3617\n",
      "                ind3_5           0.5390  0.9121  0.1779  0.0400  0.3599\n",
      "                intention_1      0.0000  0.0661  0.0000  0.0000  0.0307\n",
      "                intention_2      0.0000  0.0330  0.0051  0.1039  0.0000\n",
      "                intention_3      0.0313  0.0000  0.1009  0.2565  0.1079\n",
      "                intention_4      0.8282  0.0952  0.7467  0.5633  0.2560\n",
      "                intention_5      0.1405  0.8057  0.1474  0.0763  0.6055\n",
      "                riskp_1          0.0192  0.0716  0.0000  0.0173  0.3325\n",
      "                riskp_2          0.0402  0.1042  0.0377  0.3499  0.1740\n",
      "                riskp_3          0.0000  0.2192  0.3164  0.5475  0.3478\n",
      "                riskp_4          0.6564  0.1152  0.6460  0.0853  0.1078\n",
      "                riskp_5          0.2842  0.4899  0.0000  0.0000  0.0379\n",
      "                trust_1          0.0112  0.0000  0.0215  0.1053  0.0000\n",
      "                trust_2          0.0003  0.0165  0.0670  0.2966  0.0000\n",
      "                trust_3          0.0000  0.1096  0.4770  0.5981  0.2288\n",
      "                trust_4          0.7562  0.1915  0.4295  0.0000  0.5602\n",
      "                trust_5          0.2323  0.6824  0.0051  0.0000  0.2110\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.23\n",
      "        Class 2 : 0.14\n",
      "        Class 3 : 0.41\n",
      "        Class 4 : 0.14\n",
      "        Class 5 : 0.08\n",
      "    ============================================================================\n",
      "    Fit for 5 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 5\n",
      "    Number of estimated parameters: 204\n",
      "    Log-likelihood (LL)           : -3435.7547\n",
      "    -2LL                          : 6871.5095\n",
      "    Average LL                    : -8.1416\n",
      "    AIC                           : 7279.51\n",
      "    BIC                           : 8104.69\n",
      "    CAIC                          : 8308.69\n",
      "    Sample-Size Adjusted BIC      : 8690.51\n",
      "    Entropy                       : 85.4566\n",
      "    Scaled Relative Entropy       : 0.8742\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x1300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize an empty list to store the results\n",
    "results = []\n",
    "\n",
    "# Loop over different n_components values\n",
    "for n_components in [2, 3, 4, 5]:\n",
    "    model = StepMix(n_components=n_components, measurement=\"categorical\", verbose=1, random_state=42, structural='multinoulli', n_steps=2, assignment='soft', correction='BCH')\n",
    "    model.fit(df)\n",
    "    results.append(model.get_mm_df())\n",
    "\n",
    "# Create a subplot for each result\n",
    "fig, axs = plt.subplots(1, len(results), figsize=(28, 13))  # Increased image size\n",
    "\n",
    "# Loop over the results and create a matshow for each\n",
    "for i, result in enumerate(results):\n",
    "    axs[i].matshow(result, cmap='coolwarm')\n",
    "    axs[i].set_title(f'n_components = {i+2}')\n",
    "plt.savefig('biosecurity_plot.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting StepMix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializations (n_init) : 100%|█| 1/1 [00:00<00:00,  4.52it/s, max_LL=-3.56e+3,"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MODEL REPORT\n",
      "================================================================================\n",
      "    ============================================================================\n",
      "    Measurement model parameters\n",
      "    ============================================================================\n",
      "          model_name        categorical                \n",
      "          class_no                    0       1       2\n",
      "          param variable                               \n",
      "          pis   confid_1         0.1073  0.0000  0.0583\n",
      "                confid_2         0.3271  0.0090  0.0694\n",
      "                confid_3         0.4013  0.2692  0.1359\n",
      "                confid_4         0.1407  0.6755  0.3545\n",
      "                confid_5         0.0236  0.0463  0.3818\n",
      "                fid_1            0.0111  0.0000  0.0000\n",
      "                fid_2            0.1443  0.0268  0.0555\n",
      "                fid_3            0.5102  0.1379  0.0304\n",
      "                fid_4            0.2472  0.7498  0.3251\n",
      "                fid_5            0.0872  0.0855  0.5891\n",
      "                ind1_1           0.0111  0.0000  0.0092\n",
      "                ind1_2           0.1899  0.0086  0.0000\n",
      "                ind1_3           0.5079  0.0730  0.0000\n",
      "                ind1_4           0.2612  0.8197  0.2804\n",
      "                ind1_5           0.0299  0.0987  0.7104\n",
      "                ind2_1           0.0000  0.0000  0.0184\n",
      "                ind2_2           0.2298  0.0083  0.0411\n",
      "                ind2_3           0.4340  0.2137  0.0389\n",
      "                ind2_4           0.2740  0.5658  0.3404\n",
      "                ind2_5           0.0622  0.2122  0.5612\n",
      "                ind3_1           0.0000  0.0000  0.0276\n",
      "                ind3_2           0.0667  0.0000  0.0000\n",
      "                ind3_3           0.3151  0.1104  0.0000\n",
      "                ind3_4           0.5448  0.6063  0.2079\n",
      "                ind3_5           0.0734  0.2833  0.7646\n",
      "                intention_1      0.0000  0.0000  0.0460\n",
      "                intention_2      0.0778  0.0000  0.0184\n",
      "                intention_3      0.2310  0.0698  0.0241\n",
      "                intention_4      0.5488  0.7831  0.3015\n",
      "                intention_5      0.1425  0.1471  0.6100\n",
      "                riskp_1          0.0578  0.0000  0.1176\n",
      "                riskp_2          0.2506  0.0357  0.1147\n",
      "                riskp_3          0.5831  0.1895  0.1491\n",
      "                riskp_4          0.1085  0.7315  0.1650\n",
      "                riskp_5          0.0000  0.0433  0.4536\n",
      "                trust_1          0.0854  0.0148  0.0000\n",
      "                trust_2          0.2258  0.0387  0.0096\n",
      "                trust_3          0.5678  0.3199  0.0780\n",
      "                trust_4          0.0935  0.5718  0.3856\n",
      "                trust_5          0.0276  0.0547  0.5268\n",
      "    ============================================================================\n",
      "    Class weights\n",
      "    ============================================================================\n",
      "        Class 1 : 0.21\n",
      "        Class 2 : 0.53\n",
      "        Class 3 : 0.26\n",
      "    ============================================================================\n",
      "    Fit for 3 latent classes\n",
      "    ============================================================================\n",
      "    Estimation method             : 2-step\n",
      "    Number of observations        : 422\n",
      "    Number of latent classes      : 3\n",
      "    Number of estimated parameters: 122\n",
      "    Log-likelihood (LL)           : -3555.8945\n",
      "    -2LL                          : 7111.7889\n",
      "    Average LL                    : -8.4263\n",
      "    AIC                           : 7355.79\n",
      "    BIC                           : 7849.28\n",
      "    CAIC                          : 7971.28\n",
      "    Sample-Size Adjusted BIC      : 8199.62\n",
      "    Entropy                       : 67.6939\n",
      "    Scaled Relative Entropy       : 0.8540\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Restimating the latent class model\n",
    "model = StepMix(n_components = 3, measurement = \"categorical\", verbose = 1, random_state = 42, structural='multinoulli',n_steps=2, assignment='soft',\n",
    "correction='BCH')\n",
    "model.fit(df)\n",
    "# Class predictions\n",
    "df['categorical_pred'] = model.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>class_no</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_name</th>\n",
       "      <th>param</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"40\" valign=\"top\">categorical</th>\n",
       "      <th rowspan=\"40\" valign=\"top\">pis</th>\n",
       "      <th>confid_1</th>\n",
       "      <td>0.107</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confid_2</th>\n",
       "      <td>0.327</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confid_3</th>\n",
       "      <td>0.401</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confid_4</th>\n",
       "      <td>0.141</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confid_5</th>\n",
       "      <td>0.024</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fid_1</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fid_2</th>\n",
       "      <td>0.144</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fid_3</th>\n",
       "      <td>0.510</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fid_4</th>\n",
       "      <td>0.247</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fid_5</th>\n",
       "      <td>0.087</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1_1</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1_2</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1_3</th>\n",
       "      <td>0.508</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1_4</th>\n",
       "      <td>0.261</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1_5</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2_1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2_2</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2_3</th>\n",
       "      <td>0.434</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2_4</th>\n",
       "      <td>0.274</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2_5</th>\n",
       "      <td>0.062</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3_1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3_2</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3_3</th>\n",
       "      <td>0.315</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3_4</th>\n",
       "      <td>0.545</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3_5</th>\n",
       "      <td>0.073</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_2</th>\n",
       "      <td>0.078</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_3</th>\n",
       "      <td>0.231</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_4</th>\n",
       "      <td>0.549</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_5</th>\n",
       "      <td>0.142</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riskp_1</th>\n",
       "      <td>0.058</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riskp_2</th>\n",
       "      <td>0.251</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riskp_3</th>\n",
       "      <td>0.583</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riskp_4</th>\n",
       "      <td>0.109</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>riskp_5</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_1</th>\n",
       "      <td>0.085</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_2</th>\n",
       "      <td>0.226</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_3</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_4</th>\n",
       "      <td>0.094</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trust_5</th>\n",
       "      <td>0.028</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class_no                           0      1      2\n",
       "model_name  param variable                        \n",
       "categorical pis   confid_1     0.107  0.000  0.058\n",
       "                  confid_2     0.327  0.009  0.069\n",
       "                  confid_3     0.401  0.269  0.136\n",
       "                  confid_4     0.141  0.675  0.354\n",
       "                  confid_5     0.024  0.046  0.382\n",
       "                  fid_1        0.011  0.000  0.000\n",
       "                  fid_2        0.144  0.027  0.055\n",
       "                  fid_3        0.510  0.138  0.030\n",
       "                  fid_4        0.247  0.750  0.325\n",
       "                  fid_5        0.087  0.085  0.589\n",
       "                  ind1_1       0.011  0.000  0.009\n",
       "                  ind1_2       0.190  0.009  0.000\n",
       "                  ind1_3       0.508  0.073  0.000\n",
       "                  ind1_4       0.261  0.820  0.280\n",
       "                  ind1_5       0.030  0.099  0.710\n",
       "                  ind2_1       0.000  0.000  0.018\n",
       "                  ind2_2       0.230  0.008  0.041\n",
       "                  ind2_3       0.434  0.214  0.039\n",
       "                  ind2_4       0.274  0.566  0.340\n",
       "                  ind2_5       0.062  0.212  0.561\n",
       "                  ind3_1       0.000  0.000  0.028\n",
       "                  ind3_2       0.067  0.000  0.000\n",
       "                  ind3_3       0.315  0.110  0.000\n",
       "                  ind3_4       0.545  0.606  0.208\n",
       "                  ind3_5       0.073  0.283  0.765\n",
       "                  intention_1  0.000  0.000  0.046\n",
       "                  intention_2  0.078  0.000  0.018\n",
       "                  intention_3  0.231  0.070  0.024\n",
       "                  intention_4  0.549  0.783  0.302\n",
       "                  intention_5  0.142  0.147  0.610\n",
       "                  riskp_1      0.058  0.000  0.118\n",
       "                  riskp_2      0.251  0.036  0.115\n",
       "                  riskp_3      0.583  0.190  0.149\n",
       "                  riskp_4      0.109  0.731  0.165\n",
       "                  riskp_5      0.000  0.043  0.454\n",
       "                  trust_1      0.085  0.015  0.000\n",
       "                  trust_2      0.226  0.039  0.010\n",
       "                  trust_3      0.568  0.320  0.078\n",
       "                  trust_4      0.094  0.572  0.386\n",
       "                  trust_5      0.028  0.055  0.527"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measurement parameters from the model\n",
    "mm = model.get_mm_df()\n",
    "\n",
    "mm.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='class_no', ylabel='model_name-param-variable'>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of the Measurement parameters for the 3 class latent class model\n",
    "sns.heatmap(mm, annot=False, fmt=\".1f\", cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>model_name</th>\n",
       "      <th>param</th>\n",
       "      <th>class_no</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">measurement</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">categorical</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">pis</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>intention_1</th>\n",
       "      <td>1.000000e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_2</th>\n",
       "      <td>7.777749e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_3</th>\n",
       "      <td>2.309847e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_4</th>\n",
       "      <td>5.487614e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intention_5</th>\n",
       "      <td>1.424763e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>confid_4</th>\n",
       "      <td>3.544725e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confid_5</th>\n",
       "      <td>3.818322e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">class_weights</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">class_weights</th>\n",
       "      <th>0</th>\n",
       "      <th>nan</th>\n",
       "      <td>2.132563e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>nan</th>\n",
       "      <td>5.290006e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>nan</th>\n",
       "      <td>2.577431e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     value\n",
       "model       model_name    param         class_no variable                 \n",
       "measurement categorical   pis           0        intention_1  1.000000e-15\n",
       "                                                 intention_2  7.777749e-02\n",
       "                                                 intention_3  2.309847e-01\n",
       "                                                 intention_4  5.487614e-01\n",
       "                                                 intention_5  1.424763e-01\n",
       "...                                                                    ...\n",
       "                                        2        confid_4     3.544725e-01\n",
       "                                                 confid_5     3.818322e-01\n",
       "            class_weights class_weights 0        nan          2.132563e-01\n",
       "                                        1        nan          5.290006e-01\n",
       "                                        2        nan          2.577431e-01\n",
       "\n",
       "[123 rows x 1 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Also note that all parameters can also be recovered in a long-form dataframe. Recall we can fit both a measurement model and a structural model using the same StepMix estimator (see the first tutorial). These parameters will be available indexed by the measurement and structural keys, respectively.\n",
    "model.get_parameters_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>categorical_pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.47</td>\n",
       "      <td>10.53</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.19</td>\n",
       "      <td>25.81</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.02</td>\n",
       "      <td>80.17</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.94</td>\n",
       "      <td>20.59</td>\n",
       "      <td>76.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "categorical_pred      0      1      2\n",
       "ind1                                 \n",
       "1                 50.00   0.00  50.00\n",
       "2                 89.47  10.53   0.00\n",
       "3                 74.19  25.81   0.00\n",
       "4                  8.02  80.17  11.81\n",
       "5                  2.94  20.59  76.47"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Class predictions\n",
    "pd.crosstab(df['ind1'], df['categorical_pred'], normalize='index').mul(100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the rand score\n",
    "rand_score(df['ind1'], df['categorical_pred']).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45486961209316984"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#estimating the variance across the classes\n",
    "variance = np.var(df[\"categorical_pred\"])\n",
    "variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA test result for intention: F-value = 23.1406, p-value = 0.0000\n",
      "ANOVA test result for ind1: F-value = 203.0206, p-value = 0.0000\n",
      "ANOVA test result for fid: F-value = 77.7252, p-value = 0.0000\n",
      "ANOVA test result for ind2: F-value = 65.2826, p-value = 0.0000\n",
      "ANOVA test result for ind3: F-value = 62.1917, p-value = 0.0000\n",
      "ANOVA test result for trust: F-value = 120.8024, p-value = 0.0000\n",
      "ANOVA test result for riskp: F-value = 46.4250, p-value = 0.0000\n",
      "ANOVA test result for confid: F-value = 75.5754, p-value = 0.0000\n"
     ]
    }
   ],
   "source": [
    "#Differences across the classes using ANOVA\n",
    "class_1_data = df[df['categorical_pred'] == 0]\n",
    "class_2_data = df[df['categorical_pred'] == 1]\n",
    "class_3_data = df[df['categorical_pred'] == 2]\n",
    "\n",
    "variables = ['intention', 'ind1', 'fid', 'ind2', 'ind3', 'trust', 'riskp', 'confid']\n",
    "for variable in variables:\n",
    "    f_value, p_value = f_oneway(class_1_data[variable], class_2_data[variable], class_3_data[variable])\n",
    "    print(f\"ANOVA test result for {variable}: F-value = {f_value:.4f}, p-value = {p_value:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients are significant indicating there are statistical difference across the 3 classes. Now we can implement a post0hoc analysis to see where those differences are by performing pairwise comparisons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Yukey Honestly Significant Diffference test (HSD), test where the differences are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pairwise comparisons for intention:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "==================================================\n",
      "group1 group2 meandiff p-adj  lower  upper  reject\n",
      "--------------------------------------------------\n",
      "     0      1    0.346 0.0003 0.1377 0.5544   True\n",
      "     0      2    0.688    0.0 0.4494 0.9266   True\n",
      "     1      2    0.342 0.0001 0.1491 0.5349   True\n",
      "--------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for ind1:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "=================================================\n",
      "group1 group2 meandiff p-adj lower  upper  reject\n",
      "-------------------------------------------------\n",
      "     0      1   0.9346   0.0 0.7683 1.1009   True\n",
      "     0      2   1.6312   0.0 1.4407 1.8216   True\n",
      "     1      2   0.6966   0.0 0.5426 0.8506   True\n",
      "-------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for fid:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "=================================================\n",
      "group1 group2 meandiff p-adj lower  upper  reject\n",
      "-------------------------------------------------\n",
      "     0      1   0.7106   0.0 0.5045 0.9167   True\n",
      "     0      2   1.2509   0.0 1.0149  1.487   True\n",
      "     1      2   0.5403   0.0 0.3494 0.7312   True\n",
      "-------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for ind2:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "=================================================\n",
      "group1 group2 meandiff p-adj lower  upper  reject\n",
      "-------------------------------------------------\n",
      "     0      1   0.8343   0.0 0.6045  1.064   True\n",
      "     0      2   1.2623   0.0 0.9993 1.5254   True\n",
      "     1      2   0.4281   0.0 0.2154 0.6408   True\n",
      "-------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for ind3:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "=================================================\n",
      "group1 group2 meandiff p-adj lower  upper  reject\n",
      "-------------------------------------------------\n",
      "     0      1     0.57   0.0 0.3714 0.7686   True\n",
      "     0      2   1.0776   0.0 0.8502  1.305   True\n",
      "     1      2   0.5076   0.0 0.3237 0.6915   True\n",
      "-------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for trust:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "=================================================\n",
      "group1 group2 meandiff p-adj lower  upper  reject\n",
      "-------------------------------------------------\n",
      "     0      1   0.8264   0.0 0.6047 1.0482   True\n",
      "     0      2   1.6718   0.0 1.4178 1.9258   True\n",
      "     1      2   0.8454   0.0   0.64 1.0507   True\n",
      "-------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for riskp:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "==================================================\n",
      "group1 group2 meandiff p-adj  lower  upper  reject\n",
      "--------------------------------------------------\n",
      "     0      1   1.0636   0.0  0.7952  1.332   True\n",
      "     0      2   1.0197   0.0  0.7123  1.327   True\n",
      "     1      2   -0.044 0.909 -0.2925 0.2045  False\n",
      "--------------------------------------------------\n",
      "\n",
      "Pairwise comparisons for confid:\n",
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "===================================================\n",
      "group1 group2 meandiff p-adj   lower  upper  reject\n",
      "---------------------------------------------------\n",
      "     0      1   1.1508    0.0  0.9062 1.3955   True\n",
      "     0      2   1.3299    0.0  1.0498 1.6101   True\n",
      "     1      2   0.1791 0.1518 -0.0474 0.4056  False\n",
      "---------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Post hoc analysis\n",
    "variables = ['intention', 'ind1', 'fid', 'ind2', 'ind3', 'trust', 'riskp', 'confid']\n",
    "group_labels = df['categorical_pred'].astype(str)\n",
    "\n",
    "for variable in variables:\n",
    "    tukey_result = pairwise_tukeyhsd(df[variable], group_labels)\n",
    "    print(f\"Pairwise comparisons for {variable}:\")\n",
    "    print(tukey_result.summary())\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting the dataframe to excel\n",
    "df.to_excel('ot.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21325631, 0.52900062, 0.25774307])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#estimated class weights\n",
    "params = model.get_parameters()\n",
    "params[\"weights\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['max_n_outcomes', 'total_outcomes', 'pis'])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Item response probabilities\n",
    "params[\"measurement\"].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e-15, 1.00000000e-15, 7.77774877e-02, 2.30984741e-01,\n",
       "        5.48761425e-01, 1.42476346e-01, 1.00000000e-15, 1.11118952e-02,\n",
       "        1.89903336e-01, 5.07897350e-01, 2.61206667e-01, 2.98807516e-02,\n",
       "        1.00000000e-15, 1.11118319e-02, 1.44307744e-01, 5.10200838e-01,\n",
       "        2.47223965e-01, 8.71556208e-02, 1.00000000e-15, 1.00000000e-15,\n",
       "        2.29776590e-01, 4.34002150e-01, 2.74049660e-01, 6.21716006e-02,\n",
       "        1.00000000e-15, 1.00000000e-15, 6.66709917e-02, 3.15090210e-01,\n",
       "        5.44822203e-01, 7.34165953e-02, 1.00000000e-15, 8.53960377e-02,\n",
       "        2.25750308e-01, 5.67756031e-01, 9.35262838e-02, 2.75713402e-02,\n",
       "        1.00000000e-15, 5.78262141e-02, 2.50585347e-01, 5.83080089e-01,\n",
       "        1.08508349e-01, 1.00000000e-15, 1.00000000e-15, 1.07273322e-01,\n",
       "        3.27116326e-01, 4.01307522e-01, 1.40658455e-01, 2.36443747e-02],\n",
       "       [1.00000000e-15, 1.00000000e-15, 1.00000000e-15, 6.98240889e-02,\n",
       "        7.83111918e-01, 1.47063993e-01, 1.00000000e-15, 1.00000000e-15,\n",
       "        8.55502317e-03, 7.29812339e-02, 8.19721281e-01, 9.87424624e-02,\n",
       "        1.00000000e-15, 1.00000000e-15, 2.67844642e-02, 1.37896357e-01,\n",
       "        7.49825849e-01, 8.54933290e-02, 1.00000000e-15, 1.00000000e-15,\n",
       "        8.27106238e-03, 2.13725759e-01, 5.65812191e-01, 2.12190988e-01,\n",
       "        1.00000000e-15, 1.00000000e-15, 6.81697008e-12, 1.10392012e-01,\n",
       "        6.06347633e-01, 2.83260356e-01, 1.00000000e-15, 1.48489563e-02,\n",
       "        3.87190141e-02, 3.19925144e-01, 5.71768663e-01, 5.47382222e-02,\n",
       "        1.00000000e-15, 1.00000000e-15, 3.57294582e-02, 1.89519360e-01,\n",
       "        7.31465790e-01, 4.32853922e-02, 1.00000000e-15, 1.00000000e-15,\n",
       "        9.00497868e-03, 2.69220751e-01, 6.75450747e-01, 4.63235240e-02],\n",
       "       [1.00000000e-15, 4.59695816e-02, 1.83922476e-02, 2.41369140e-02,\n",
       "        3.01506509e-01, 6.09994747e-01, 1.00000000e-15, 9.19386395e-03,\n",
       "        1.00000000e-15, 1.00000000e-15, 2.80412346e-01, 7.10393790e-01,\n",
       "        1.00000000e-15, 1.00000000e-15, 5.54745129e-02, 3.03505913e-02,\n",
       "        3.25110373e-01, 5.89064523e-01, 1.00000000e-15, 1.83878326e-02,\n",
       "        4.11430593e-02, 3.88955384e-02, 3.40415792e-01, 5.61157777e-01,\n",
       "        1.00000000e-15, 2.75817489e-02, 1.00000000e-15, 1.00000000e-15,\n",
       "        2.07867012e-01, 7.64551239e-01, 1.00000000e-15, 1.00000000e-15,\n",
       "        9.56374122e-03, 7.80171748e-02, 3.85616186e-01, 5.26802898e-01,\n",
       "        1.00000000e-15, 1.17645154e-01, 1.14672037e-01, 1.49108993e-01,\n",
       "        1.64973160e-01, 4.53600656e-01, 1.00000000e-15, 5.83448443e-02,\n",
       "        6.94249601e-02, 1.35925559e-01, 3.54472453e-01, 3.81832183e-01]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[\"measurement\"][\"pis\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of parameters\n",
    "params['measurement']['total_outcomes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of outcomes\n",
    "params['measurement']['max_n_outcomes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 48)\n",
      "(3, 9)\n"
     ]
    }
   ],
   "source": [
    "#Shape of the parameters and components\n",
    "print(params[\"measurement\"][\"pis\"].shape)\n",
    "print((model.n_components, df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>categorical_pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.47</td>\n",
       "      <td>10.53</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.19</td>\n",
       "      <td>25.81</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.02</td>\n",
       "      <td>80.17</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.94</td>\n",
       "      <td>20.59</td>\n",
       "      <td>76.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "categorical_pred      0      1      2\n",
       "ind1                                 \n",
       "1                 50.00   0.00  50.00\n",
       "2                 89.47  10.53   0.00\n",
       "3                 74.19  25.81   0.00\n",
       "4                  8.02  80.17  11.81\n",
       "5                  2.94  20.59  76.47"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Crosstab \n",
    "pd.crosstab(df['ind1'], df['categorical_pred'], normalize='index').mul(100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7232047370850266"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the rand score\n",
    "rand_score(df['ind1'], df['categorical_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>categorical_pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.07</td>\n",
       "      <td>7.41</td>\n",
       "      <td>18.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.76</td>\n",
       "      <td>56.04</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.77</td>\n",
       "      <td>67.55</td>\n",
       "      <td>19.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.51</td>\n",
       "      <td>42.98</td>\n",
       "      <td>53.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "categorical_pred      0      1       2\n",
       "ind2                                  \n",
       "1                  0.00   0.00  100.00\n",
       "2                 74.07   7.41   18.52\n",
       "3                 41.76  56.04    2.20\n",
       "4                 12.77  67.55   19.68\n",
       "5                  3.51  42.98   53.51"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['ind2'], df['categorical_pred'], normalize='index').mul(100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5989463137868537"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_score(df['ind2'], df['categorical_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>categorical_pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind3</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.83</td>\n",
       "      <td>47.17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.22</td>\n",
       "      <td>67.15</td>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.92</td>\n",
       "      <td>42.48</td>\n",
       "      <td>53.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "categorical_pred       0      1       2\n",
       "ind3                                   \n",
       "1                   0.00   0.00  100.00\n",
       "2                 100.00   0.00    0.00\n",
       "3                  52.83  47.17    0.00\n",
       "4                  22.22  67.15   10.63\n",
       "5                   3.92  42.48   53.59"
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     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.crosstab(df['ind3'], df['categorical_pred'], normalize='index').mul(100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 220,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 221,
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       "      <th>3</th>\n",
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       "categorical_pred       0      1      2\n",
       "fid                                   \n",
       "1                 100.00   0.00   0.00\n",
       "2                  52.00  24.00  24.00\n",
       "3                  58.75  37.50   3.75\n",
       "4                   8.00  76.44  15.56\n",
       "5                   7.69  23.08  69.23"
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    "pd.crosstab(df['fid'], df['categorical_pred'], normalize='index').mul(100).round(2)"
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  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
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     "execution_count": 222,
     "metadata": {},
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       "      <th>3</th>\n",
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       "categorical_pred      0      1      2\n",
       "trust                                \n",
       "1                 63.64  36.36   0.00\n",
       "2                 63.33  33.33   3.33\n",
       "3                 38.17  55.73   6.11\n",
       "4                  3.93  73.03  23.03\n",
       "5                  4.17  16.67  79.17"
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     "metadata": {},
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     "execution_count": 224,
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   "execution_count": 225,
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       "categorical_pred      0      1       2\n",
       "intention                             \n",
       "1                  0.00   0.00  100.00\n",
       "2                 77.78   0.00   22.22\n",
       "3                 53.85  41.03    5.13\n",
       "4                 17.90  69.65   12.45\n",
       "5                 10.71  30.36   58.93"
      ]
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     "execution_count": 225,
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       "categorical_pred      0      1      2\n",
       "riskp                                \n",
       "1                 27.78   0.00  72.22\n",
       "2                 53.49  16.28  30.23\n",
       "3                 45.05  41.44  13.51\n",
       "4                  4.19  87.96   7.85\n",
       "5                  0.00  13.56  86.44"
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     "execution_count": 226,
     "metadata": {},
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    "pd.crosstab(df['riskp'], df['categorical_pred'], normalize='index').mul(100).round(2)"
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  {
   "cell_type": "code",
   "execution_count": 227,
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     "execution_count": 227,
     "metadata": {},
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   "source": [
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   "cell_type": "code",
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       "categorical_pred      0      1      2\n",
       "confid                               \n",
       "1                 62.50   0.00  37.50\n",
       "2                 74.36   5.13  20.51\n",
       "3                 30.63  56.76  12.61\n",
       "4                  5.45  75.74  18.81\n",
       "5                  3.70  20.37  75.93"
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     "execution_count": 228,
     "metadata": {},
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    "pd.crosstab(df['confid'], df['categorical_pred'], normalize='index').mul(100).round(2)"
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     "execution_count": 229,
     "metadata": {},
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